Project Summary Our proposed studies will address major clinical challenges associated with differential drug treatment response, focusing on advanced prostate cancer. Our overall approach is based upon the co-clinical trial paradigm, in which genetically-engineered mouse (GEM) models are assayed for drug response to provide information that can be incorporated into patient treatment regimens. Here, we propose a novel augmented co- clinical paradigm that uses analyses of tissue-specific organoids together with GEM models to expedite investigation of differential drug response and drug synergy, as well as sophisticated computational systems biology approaches to identify molecular regulators of drug response that are conserved from mouse models to human cancer. In preliminary studies, we have established methods for novel three-dimensional culture of tumor organoids, which display drug responses characteristic of the GEM models from which they were established. Furthermore, we have used computational systems methods to generate gene regulatory networks (interactomes) for both mouse and human prostate cancer, and have demonstrated their utility for cross-species identification of candidate master regulators of tumor aggressiveness. We have expanded these systems biology approaches for prediction of drug response in preclinical studies and to extrapolate these data to human prostate cancer. Based on these preliminary studies, we hypothesize that systematic analysis of drug response in genetically-engineered mouse (GEM) models followed by cross-species systems analyses can inform human cancer treatment by enabling the systematic evaluation of optimal drug treatments in distinct tumor contexts as well as by identifying patients who are most likely to respond to treatment. Thus, our proposed research will pursue the broad objective of identifying the underlying mechanisms of differential drug response in distinct prostate tumor contexts. Our specific plans are: Aim 1: To identify optimal drug treatments for specific tumor contexts, we will use organoid lines derived from a series of GEM models of prostate cancer to assay response to a range of drugs, including agents currently used for treatment of advanced prostate cancer. These findings from organoid models will be experimentally validated in GEM models in vivo. Aim 2: To analyze molecular mechanisms of drug response and drug synergy, we will use cross-species computational systems approaches to identify genes and pathways that regulate drug response in human prostate cancer. These results will be experimentally validated in organoid, GEM, and xenograft models. Impact: Our proposed studies directly address the broad goals of the Oncology Models Forum, since they introduce new experimental paradigms for effective translation of mouse models to achieve unmet translational needs. Our newly developed organoid models and cross-species computational analyses and validation will be of broad value to members of the Oncology Forum and are sharable through the NCIP Hub.